Development and Validation of a Machine Learning Model for Predicting Long-Term Depression Risk in ACS Patients After PCI: A Retrospective Cohort Study by Lv H, Sun F, Zhang Y, Zhou X

“…These models used routine clinical information like heart function, thyroid levels, and diabetes status to make predictions.The results showed that about 1 in 5 patients developed depression. …”
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Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior by Luis H. Quitian-Ardila, Yamid J. Garcia-Blanco, Angel De J. Rivera, Raquel S. Schimicoscki, Muhammad Nadeem, Oriana Palma Calabokis, Vladimir Ballesteros-Ballesteros, Admilson T. Franco

“…Different configurations with different hidden layers, using neuron sequences of 16, 32, and 64, learning rates of 0.001 and 0.01, and the ReLU activation function were used to improve the model's performance. …”
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HyperKon: A Self-Supervised Contrastive Network for Hyperspectral Image Analysis by Daniel La’ah Ayuba, Jean-Yves Guillemaut, Belen Marti-Cardona, Oscar Mendez

“…The use of a pretrained image classification model (trained on cats and dogs, for example) as a perceptual loss function for hyperspectral super-resolution and pansharpening tasks is surprisingly effective. …”
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Charting the course of digital collaboration: a bibliometric analysis of COIL literature by Adya Sharma, Nehajoan Panackal

“…Collaborative Online International Learning (COIL) is a sustainable solution in which two cohorts of faculty and students from geographically and culturally diverse backgrounds can collaborate and interact thereby gaining skills to function in a multicultural and diverse global community. …”
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Flow Cytometric Assessment of FcγRIIIa-V158F Polymorphisms and NK Cell Mediated ADCC Revealed Reduced NK Cell Functionality in Colorectal Cancer Patients by Phillip Schiele, Stefan Kolling, Stanislav Rosnev, Charlotte Junkuhn, Anna Luzie Walter, Jobst Christian von Einem, Sebastian Stintzing, Wenzel Schöning, Igor Maximilian Sauer, Dominik Paul Modest, Kathrin Heinrich, Lena Weiss, Volker Heinemann, Lars Bullinger, Marco Frentsch, Il-Kang Na

“…Fc gamma receptor IIIa (FcγRIIIa) polymorphisms impact ADCC, yet their clinical relevance in NK cell functionality remains debated. We developed two complementary flow cytometry assays: one to predict the FcγRIIIa-V158F polymorphism using a machine learning model, and a 15-color flow cytometry panel to assess antibody-induced NK cell functionality and cancer-immune cell interactions. …”
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Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection by Qifan Yang, Yu-Ang Chen, Yifan Shi

“…Specifically, a multi-view co-association matrix optimization objective function with doubly-stochastic constraints is first designed to capture the cross-view cluster structure. …”
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The Development of Ecological Identities in Children’s Books: A Linguistic Approach to Character Positioning as Eco-Rebels by Corinna Lüdicke

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A novel network with enhanced edge information for left atrium segmentation from LGE-MRI by Ze Zhang, Zhen Wang, Xiqian Wang, Kuanquan Wang, Yongfeng Yuan, Qince Li

“…The design of EIEM is grounded in exploring edge details within target region features learned from images. Additionally, it incorporates a Spatially Weighted Cross-Entropy loss function tailored for EIEM, introducing constraints on different regions based on the importance of pixels to edge segmentation, while also mitigating class imbalance through weighted treatment of positive and negative samples.ResultsThe proposed method is validated on the 2018 Atrial Segmentation Challenge dataset. …”
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Mediating role of gut microbiota on Baduanjin for cognitive function in community-dwelling older adults with mild cognitive impairment: a study protocol for a cluster randomised co... by Guohua Zheng, Liwei Sun, Qian Geng

“…Four residential communities (clusters) with a total of 64 eligible participants (16 participants in each residential community) will be randomised 1:1 to either the 24-week Baduanjin exercise intervention group (60 min per session, four sessions per week) or the no specific exercise intervention control group. Global cognitive function and the subdimensions of cognition including executive function, visuospatial function, language function and memory function will be assessed using the Montreal Cognitive Assessment Scale, Trail Making Test, Auditory Verbal Learning Test, Boston Naming Test-30 and Clock-Drawing Test, while the gut microbiome and its metabolomics will be detected using 16S rRNA and ultra-high-performance liquid chromatography–tandem mass spectrometry at baseline and at the end of 24-week intervention. …”
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Dynamic Adjacency Matrix Learning and Multi-Order Random Walk Aggregation for Alzheimer’s Disease Diagnosis From Resting-State fMRI by Zhaoyang Guan, Jierui Li, Yuxin Liu, Xianrui Shan

“…Alzheimer’s disease (AD) is a progressive, irreversible neurodegenerative disorder, and early diagnosis is critical for timely intervention and slowing down the course of the disease. Functional magnetic resonance imaging (fMRI), a non-invasive neuroimaging technique, can be used to detect functional brain activity. …”
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Corrigendum to “Inhibition of NADPH oxidase by apocynin prevents learning and memory deficits in a mouse Parkinson's disease model” [Redox Biol. 22 (2019): 101134] by Liyan Hou, Fuqiang Sun, Ruixue Huang, Wei Sun, Dan Zhang, Qingshan Wang

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Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma by Duo Wang, Duo Wang, Duo Wang, Jihao Tu, Jihao Tu, Jianfeng Liu, Jianfeng Liu, Yuting Piao, Yuting Piao, Yiming Zhao, Yiming Zhao, Ying Xiong, Ying Xiong, Jianing Wang, Jianing Wang, Xiaotian Zheng, Xiaotian Zheng, Bin Liu, Bin Liu

“…Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.ResultsWe identified 151 GPR-related genes at both the single-cell and bulk transcriptome levels, and identified a Stepglm[both]+Enet[alpha=0.6] model with seven GPR-related genes as the final diagnostic predictive model with high accuracy and translational relevance using a 127-combination machine learning computational framework, and the GPR-integrated diagnosis nomogram provided a quantitative tool in clinical practice. …”
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Diagnostic biomarkers and immune infiltration profiles common to COVID-19, acute myocardial infarction and acute ischaemic stroke using bioinformatics methods and machine learning by Ya-Nan Ma, Si-Rong Ma, Li Yang, Juan Wu, Ya-Rong Wang, Li-Jia Bao, Li Ma, Qing-Qiu Wu, Zhen-Hai Wang

“…Finally, genes selected through machine learning methods included: CLEC4E, S100A12, and IL1R2. …”
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SugarViT-Multi-objective regression of UAV images with Vision Transformers and Deep Label Distribution Learning demonstrated on disease severity prediction in sugar beet. by Maurice Günder, Facundo Ramón Ispizua Yamati, Abel Barreto, Anne-Katrin Mahlein, Rafet Sifa, Christian Bauckhage

“…With concepts of DLDL, special loss functions, and a tailored model architecture, we develop an efficient Vision Transformer based model for disease severity scoring called SugarViT. …”
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Mechanistic role of pyroptosis in Kawasaki disease: An integrative bioinformatics analysis of immune dysregulation, machine learning-based biomarker discovery, WGCNA, and drug repu... by Chen Wang, Qinchao Wu, Jie Chen, Jun Wang, Dan Li

“…Through machine learning-based biomarker discovery, we identified MYD88 and S100A12 as critical pyroptosis-related genes in KD. …”
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An Analysis of the Use of Puzzle-Based Learning Media in Enhancing Students' Interest in Science Subjects in Grade V at SD Negeri 19 Tulang Bawang Udik by Asri Nuraini, Masrurotul Mahmudah, Nurul Aisyah

“…This media not only functions as a tool to increase students' concentration, but is also an effective way to increase interest in learning. …”
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An Exploratory Study of Small-Group Learning Interactions in Pre-Clerkship Medical Education: Uncovering a Mismatch Between Student Perceptions and Real-Time Observations by Jennifer M. Walton, Anna E. Oswald

“…Small-group learning is a mainstay of medical education, and group functioning can have a major influence on these learning experiences. …”
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Body Awareness Does Not Need a Pedigree: Mixed-Breed Dogs Rely More on Self-Representation Than Social Learning in a Spatial Task by Petra Dobos, Péter Pongrácz

“…The dogs who watched the demonstration for longer in trial 3 chose the door less often in trial 4, which indicates social learning. By testing mixed-breed dogs, our results are minimally influenced by functional breed selection and serve as valuable baseline for testing complex socio-cognitive traits in companion dogs.…”
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Bioinformatics-Driven Structural and Pharmacological Analysis of SLITRK1 in Tourette Syndrome: Impact of S656M Mutation Using Molecular Dynamics, Docking, and Reinforcement Learnin... by Emre Aktaş, Alirıza İslim, Kevser Kübra Kırboğa, Derya Yıldız, Nehir Özdemir Özgentürk, Mithun Rudrapal, Johra Khan, Raghu Ram Achar, Ekaterina Silina, Natalia Manturova, Victor Stupin

“…Structural analyses revealed that the S656M mutation significantly alters SLITRK1’s 3D conformation, biological functions, and drug binding profiles. Among the tested drugs, Aripiprazole exhibited the highest binding affinity across various SLITRK1 variants, with reinforcement learning highlighting a notable interaction with the S659K mutation. …”
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Deep‐GB: A novel deep learning model for globular protein prediction using CNN‐BiLSTM architecture and enhanced PSSM with trisection strategy by Sonia Zouari, Farman Ali, Atef Masmoudi, Sarah Abu Ghazalah, Wajdi Alghamdi, Faris A. Kateb, Nouf Ibrahim

“…Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. …”
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